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## What does this PR do?
**Type of change:** New feature
**Overview:** Adds a configurable `moe_calib_experts_ratio` parameter
that controls the percentage of experts to calibrate during the forward
pass in MoE (Mixture of Experts) models. Previously, the calibration
forward always routed tokens to **all** experts, which is expensive.
This PR allows the user to specify a ratio (default: still all experts
so no behavior change) to improve expert calibration coverage without
the cost of a full-expert forward. The token counting for the expert
coverage table now tracks the calibration routing and runs on CUDA for
efficiency.
**Changes include:**
- New `moe_calib_experts_ratio` field in `QuantizeAlgorithmConfig`
(`config.py`)
- Propagation of the ratio from the algorithm config to MoE modules
during calibration (`mode.py`)
- Updated `_QuantSparseMoe.forward` to use the configurable ratio
instead of hard-coding all experts (`huggingface.py`)
- New `--moe_calib_experts_ratio` CLI flag in `hf_ptq.py` (default
`0.25`)
- Moved `expert_token_count` tensor to CUDA and updated the HTML table
title in `moe_utils.py`
## Usage
Via hf_ptq.py CLI — calibrate 50% of experts during MoE calibration
python hf_ptq.py --model <model> --qformat int4_awq
--moe_calib_experts_ratio 0.5
Via Python API — pass the ratio through the algorithm config
import modelopt.torch.quantization as mtq
quant_cfg = {
"quant_cfg": { ... },
"algorithm": {
"method": "awq_lite",
"moe_calib_experts_ratio": 0.25, # calibrate 1/4 of experts
},
}
mtq.quantize(model, quant_cfg, forward_loop=calib_loop)
## Testing
Test with Qwen3 30B A3B calibration and check the tokens per expert.
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
## Release Notes
* **New Features**
* Added support for configurable expert calibration during Mixture of
Experts (MOE) model quantization. Users can now specify the percentage
of experts to include during calibration, enabling better expert
coverage and improved quantization accuracy for MOE models. Default: 25%
of all experts.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>
Signed-off-by: Chenjie Luo <108829653+cjluo-nv@users.noreply.github.com>
Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com>
Co-authored-by: realAsma <86726418+realAsma@users.noreply.github.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
NVIDIA Model Optimizer (referred to as Model Optimizer, or ModelOpt) is a library comprising state-of-the-art model optimization techniques including quantization, distillation, pruning, speculative decoding and sparsity to accelerate models.
[Input] Model Optimizer currently supports inputs of a Hugging Face, PyTorch or ONNX model.
[Optimize] Model Optimizer provides Python APIs for users to easily compose the above model optimization techniques and export an optimized quantized checkpoint. Model Optimizer is also integrated with NVIDIA Megatron-Bridge, Megatron-LM and Hugging Face Accelerate for training required inference optimization techniques.
[Export for deployment] Seamlessly integrated within the NVIDIA AI software ecosystem, the quantized checkpoint generated from Model Optimizer is ready for deployment in downstream inference frameworks like SGLang, TensorRT-LLM, TensorRT, or vLLM. The unified Hugging Face export API now supports both transformers and diffusers models.
Latest News
- [2025/12/11] BLOG: Top 5 AI Model Optimization Techniques for Faster, Smarter Inference
- [2025/12/08] NVIDIA TensorRT Model Optimizer is now officially rebranded as NVIDIA Model Optimizer.
- [2025/10/07] BLOG: Pruning and Distilling LLMs Using NVIDIA Model Optimizer
- [2025/09/17] BLOG: An Introduction to Speculative Decoding for Reducing Latency in AI Inference
- [2025/09/11] BLOG: How Quantization Aware Training Enables Low-Precision Accuracy Recovery
- [2025/08/29] BLOG: Fine-Tuning gpt-oss for Accuracy and Performance with Quantization Aware Training
- [2025/08/01] BLOG: Optimizing LLMs for Performance and Accuracy with Post-Training Quantization
- [2025/06/24] BLOG: Introducing NVFP4 for Efficient and Accurate Low-Precision Inference
- [2025/05/14] NVIDIA TensorRT Unlocks FP4 Image Generation for NVIDIA Blackwell GeForce RTX 50 Series GPUs
- [2025/04/21] Adobe optimized deployment using Model-Optimizer + TensorRT leading to a 60% reduction in diffusion latency, a 40% reduction in total cost of ownership
- [2025/04/05] NVIDIA Accelerates Inference on Meta Llama 4 Scout and Maverick. Check out how to quantize Llama4 for deployment acceleration here
- [2025/03/18] World's Fastest DeepSeek-R1 Inference with Blackwell FP4 & Increasing Image Generation Efficiency on Blackwell
- [2025/02/25] Model Optimizer quantized NVFP4 models available on Hugging Face for download: DeepSeek-R1-FP4, Llama-3.3-70B-Instruct-FP4, Llama-3.1-405B-Instruct-FP4
- [2025/01/28] Model Optimizer has added support for NVFP4. Check out an example of NVFP4 PTQ here.
- [2025/01/28] Model Optimizer is now open source!
Previous News
- [2024/10/23] Model Optimizer quantized FP8 Llama-3.1 Instruct models available on Hugging Face for download: 8B, 70B, 405B.
- [2024/09/10] Post-Training Quantization of LLMs with NVIDIA NeMo and Model Optimizer.
- [2024/08/28] Boosting Llama 3.1 405B Performance up to 44% with Model Optimizer on NVIDIA H200 GPUs
- [2024/08/28] Up to 1.9X Higher Llama 3.1 Performance with Medusa
- [2024/08/15] New features in recent releases: Cache Diffusion, QLoRA workflow with NVIDIA NeMo, and more. Check out our blog for details.
- [2024/06/03] Model Optimizer now has an experimental feature to deploy to vLLM as part of our effort to support popular deployment frameworks. Check out the workflow here
- [2024/05/08] Announcement: Model Optimizer Now Formally Available to Further Accelerate GenAI Inference Performance
- [2024/03/27] Model Optimizer supercharges TensorRT-LLM to set MLPerf LLM inference records
- [2024/03/18] GTC Session: Optimize Generative AI Inference with Quantization in TensorRT-LLM and TensorRT
- [2024/03/07] Model Optimizer's 8-bit Post-Training Quantization enables TensorRT to accelerate Stable Diffusion to nearly 2x faster
- [2024/02/01] Speed up inference with Model Optimizer quantization techniques in TRT-LLM
Install
To install stable release packages for Model Optimizer with pip from PyPI:
pip install -U nvidia-modelopt[all]
To install from source in editable mode with all development dependencies or to use the latest features, run:
# Clone the Model Optimizer repository
git clone git@github.com:NVIDIA/Model-Optimizer.git
cd Model-Optimizer
pip install -e .[dev]
You can also directly use the TensorRT-LLM docker images
(e.g., nvcr.io/nvidia/tensorrt-llm/release:<version>), which have Model Optimizer pre-installed.
Make sure to upgrade Model Optimizer to the latest version using pip as described above.
Visit our installation guide for
more fine-grained control on installed dependencies or for alternative docker images and environment variables to setup.
Techniques
| Technique | Description | Examples | Docs |
|---|---|---|---|
| Post Training Quantization | Compress model size by 2x-4x, speeding up inference while preserving model quality! | [LLMs] [diffusers] [VLMs] [onnx] [windows] | [docs] |
| Quantization Aware Training | Refine accuracy even further with a few training steps! | [NeMo] [Hugging Face] | [docs] |
| Pruning | Reduce your model size and accelerate inference by removing unnecessary weights! | [PyTorch] | [docs] |
| Distillation | Reduce deployment model size by teaching small models to behave like larger models! | [NeMo] [Hugging Face] | [docs] |
| Speculative Decoding | Train draft modules to predict extra tokens during inference! | [Megatron] [Hugging Face] | [docs] |
| Sparsity | Efficiently compress your model by storing only its non-zero parameter values and their locations | [PyTorch] | [docs] |
Pre-Quantized Checkpoints
- Ready-to-deploy checkpoints [🤗 Hugging Face - Nvidia Model Optimizer Collection]
- Deployable on TensorRT-LLM, vLLM and SGLang
- More models coming soon!
Resources
Model Support Matrix
| Model Type | Support Matrix |
|---|---|
| LLM Quantization | View Support Matrix |
| Diffusers Quantization | View Support Matrix |
| VLM Quantization | View Support Matrix |
| ONNX Quantization | View Support Matrix |
| Windows Quantization | View Support Matrix |
| Quantization Aware Training | View Support Matrix |
| Pruning | View Support Matrix |
| Distillation | View Support Matrix |
| Speculative Decoding | View Support Matrix |
Contributing
Model Optimizer is now open source! We welcome any feedback, feature requests and PRs. Please read our Contributing guidelines for details on how to contribute to this project.
Top Contributors
Happy optimizing!